Short term load forecasting: two stage modelling

This paper studies the hourly electricity load demand in the area covered by a utility situated in the Seattle, USA, called Puget Sound Power and Light Company. Our proposal is put into proof with the famous dataset from this company. We propose a stochastic model which employs ANN (Artificial Neura...

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Main Authors: SOARES, L. J., ALMEIDA, E. J.
Format: Article
Language:English
Published: Faculdade Salesiana Maria Auxiliadora 2009-06-01
Series:Sistemas de Informação
Subjects:
Online Access:http://www.fsma.edu.br/si/edicao3/rna_paper.pdf
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author SOARES, L. J.
ALMEIDA, E. J.
author_facet SOARES, L. J.
ALMEIDA, E. J.
author_sort SOARES, L. J.
collection DOAJ
description This paper studies the hourly electricity load demand in the area covered by a utility situated in the Seattle, USA, called Puget Sound Power and Light Company. Our proposal is put into proof with the famous dataset from this company. We propose a stochastic model which employs ANN (Artificial Neural Networks) to model short-run dynamics and the dependence among adjacent hours. The model proposed treats each hour's load separately as individual single series. This approach avoids modeling the intricate intra-day pattern (load profile) displayed by the load, which varies throughout days of the week and seasons. The forecasting performance of the model is evaluated in similiar mode a TLSAR (Two-Level Seasonal Autoregressive) model proposed by Soares (2003) using the years of 1995 and 1996 as the holdout sample. Moreover, we conclude that non linearity is present in some series of these data. The model results are analyzed. The experiment shows that our tool can be used to produce load forecasting in tropical climate places.
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spelling doaj.art-747bf560b497422bb434776157ce2b072022-12-22T01:42:56ZengFaculdade Salesiana Maria AuxiliadoraSistemas de Informação1983-56042009-06-0134054Short term load forecasting: two stage modellingSOARES, L. J.ALMEIDA, E. J.This paper studies the hourly electricity load demand in the area covered by a utility situated in the Seattle, USA, called Puget Sound Power and Light Company. Our proposal is put into proof with the famous dataset from this company. We propose a stochastic model which employs ANN (Artificial Neural Networks) to model short-run dynamics and the dependence among adjacent hours. The model proposed treats each hour's load separately as individual single series. This approach avoids modeling the intricate intra-day pattern (load profile) displayed by the load, which varies throughout days of the week and seasons. The forecasting performance of the model is evaluated in similiar mode a TLSAR (Two-Level Seasonal Autoregressive) model proposed by Soares (2003) using the years of 1995 and 1996 as the holdout sample. Moreover, we conclude that non linearity is present in some series of these data. The model results are analyzed. The experiment shows that our tool can be used to produce load forecasting in tropical climate places.http://www.fsma.edu.br/si/edicao3/rna_paper.pdfNeural networksnonlinear modelsshort-term load forecastingstatistical model building.
spellingShingle SOARES, L. J.
ALMEIDA, E. J.
Short term load forecasting: two stage modelling
Sistemas de Informação
Neural networks
nonlinear models
short-term load forecasting
statistical model building.
title Short term load forecasting: two stage modelling
title_full Short term load forecasting: two stage modelling
title_fullStr Short term load forecasting: two stage modelling
title_full_unstemmed Short term load forecasting: two stage modelling
title_short Short term load forecasting: two stage modelling
title_sort short term load forecasting two stage modelling
topic Neural networks
nonlinear models
short-term load forecasting
statistical model building.
url http://www.fsma.edu.br/si/edicao3/rna_paper.pdf
work_keys_str_mv AT soareslj shorttermloadforecastingtwostagemodelling
AT almeidaej shorttermloadforecastingtwostagemodelling